Overview

Dataset statistics

Number of variables93
Number of observations591
Missing cells22495
Missing cells (%)40.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory397.2 KiB
Average record size in memory688.2 B

Variable types

Categorical20
DateTime1
Boolean46
Numeric26

Warnings

study_no has a high cardinality: 75 distinct values High cardinality
medication_list has a high cardinality: 90 distinct values High cardinality
abdominal_pain_level is highly correlated with albumin and 2 other fieldsHigh correlation
albumin is highly correlated with abdominal_pain_levelHigh correlation
haematocrit_percent is highly correlated with haemoglobin and 1 other fieldsHigh correlation
haemoglobin is highly correlated with haematocrit_percentHigh correlation
liver_size is highly correlated with abdominal_pain_levelHigh correlation
lymphocytes_percent is highly correlated with vomiting_levelHigh correlation
sbp is highly correlated with vomiting_levelHigh correlation
vomiting_level is highly correlated with abdominal_pain_level and 3 other fieldsHigh correlation
day_from_enrolment is highly correlated with day_from_admissionHigh correlation
day_from_admission is highly correlated with day_from_enrolmentHigh correlation
abdominal_tenderness has 266 (45.0%) missing values Missing
albumin has 527 (89.2%) missing values Missing
alt has 510 (86.3%) missing values Missing
antibiotics has 337 (57.0%) missing values Missing
antibiotics_list has 549 (92.9%) missing values Missing
ast has 510 (86.3%) missing values Missing
bleeding has 302 (51.1%) missing values Missing
bleeding_vaginal has 8 (1.4%) missing values Missing
blood_fluid has 444 (75.1%) missing values Missing
body_temperature has 265 (44.8%) missing values Missing
colloid has 445 (75.3%) missing values Missing
colloid_description has 581 (98.3%) missing values Missing
conjunctival_injection has 265 (44.8%) missing values Missing
coronary_heart_disease has 520 (88.0%) missing values Missing
creatine_kinase has 555 (93.9%) missing values Missing
creatinine has 509 (86.1%) missing values Missing
crystalloid has 444 (75.1%) missing values Missing
crystalloid_description has 450 (76.1%) missing values Missing
dbp has 319 (54.0%) missing values Missing
fluid_reason_other has 444 (75.1%) missing values Missing
fluid_reason_other_description has 577 (97.6%) missing values Missing
haematocrit_high has 444 (75.1%) missing values Missing
haematocrit_percent has 256 (43.3%) missing values Missing
haemoglobin has 265 (44.8%) missing values Missing
hematemesis has 264 (44.7%) missing values Missing
hypertension has 519 (87.8%) missing values Missing
igg has 463 (78.3%) missing values Missing
igg_interpretation has 463 (78.3%) missing values Missing
igm has 463 (78.3%) missing values Missing
igm_interpretation has 463 (78.3%) missing values Missing
lethargy has 264 (44.7%) missing values Missing
liver_palpation has 265 (44.8%) missing values Missing
lymphadenopathy has 266 (45.0%) missing values Missing
lymphadenopathy_specification has 566 (95.8%) missing values Missing
lymphocytes_percent has 256 (43.3%) missing values Missing
medication has 338 (57.2%) missing values Missing
medication_list has 454 (76.8%) missing values Missing
melaena has 264 (44.7%) missing values Missing
monocytes_percent has 256 (43.3%) missing values Missing
neutrophils_percent has 257 (43.5%) missing values Missing
parental_fluid_volume has 445 (75.3%) missing values Missing
pcr_dengue_load has 519 (87.8%) missing values Missing
pcr_dengue_serotype has 24 (4.1%) missing values Missing
pharyngeal_injection has 266 (45.0%) missing values Missing
platelets has 444 (75.1%) missing values Missing
plt has 256 (43.3%) missing values Missing
pulse has 269 (45.5%) missing values Missing
rales_crackles has 264 (44.7%) missing values Missing
rehydration has 444 (75.1%) missing values Missing
renal_disease has 519 (87.8%) missing values Missing
respiratory_rate has 266 (45.0%) missing values Missing
restlessness has 264 (44.7%) missing values Missing
rhinitis has 265 (44.8%) missing values Missing
sbp has 265 (44.8%) missing values Missing
serology_interpretation has 519 (87.8%) missing values Missing
shock_resucitation has 444 (75.1%) missing values Missing
skin_describe has 579 (98.0%) missing values Missing
treatment_haemorrhage has 444 (75.1%) missing values Missing
vomiting has 82 (13.9%) missing values Missing
wbc has 256 (43.3%) missing values Missing
pcr_dengue_load has 21 (3.6%) zeros Zeros
day_from_enrolment has 72 (12.2%) zeros Zeros
day_from_admission has 72 (12.2%) zeros Zeros

Reproduction

Analysis started2021-02-12 10:49:02.535065
Analysis finished2021-02-12 10:50:10.598467
Duration1 minute and 8.06 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

study_no
Categorical

HIGH CARDINALITY

Distinct75
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
20-0708
 
12
20-0718
 
11
20-0707
 
11
20-0727
 
11
20-0705
 
11
Other values (70)
535 

Length

Max length9
Median length7
Mean length7.027072758
Min length7

Characters and Unicode

Total characters4153
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.5%

Sample

1st row20 - 0762
2nd row20 - 0762
3rd row20 - 0762
4th row20 - 0762
5th row20 - 0762
ValueCountFrequency (%)
20-070812
 
2.0%
20-071811
 
1.9%
20-070711
 
1.9%
20-072711
 
1.9%
20-070511
 
1.9%
20-071411
 
1.9%
20-073711
 
1.9%
20-075110
 
1.7%
20-074810
 
1.7%
20-073310
 
1.7%
Other values (65)483
81.7%
2021-02-12T10:50:10.789041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20-070812
 
2.0%
20-071811
 
1.8%
20-072711
 
1.8%
20-070711
 
1.8%
20-073711
 
1.8%
20-071411
 
1.8%
20-070511
 
1.8%
20-076710
 
1.6%
20-073310
 
1.6%
20-074210
 
1.6%
Other values (67)499
82.2%

Most occurring characters

ValueCountFrequency (%)
01319
31.8%
2747
18.0%
7698
16.8%
-591
14.2%
1153
 
3.7%
3136
 
3.3%
6132
 
3.2%
5124
 
3.0%
4121
 
2.9%
865
 
1.6%
Other values (2)67
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3546
85.4%
Dash Punctuation591
 
14.2%
Space Separator16
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
01319
37.2%
2747
21.1%
7698
19.7%
1153
 
4.3%
3136
 
3.8%
6132
 
3.7%
5124
 
3.5%
4121
 
3.4%
865
 
1.8%
951
 
1.4%
ValueCountFrequency (%)
16
100.0%
ValueCountFrequency (%)
-591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4153
100.0%

Most frequent character per script

ValueCountFrequency (%)
01319
31.8%
2747
18.0%
7698
16.8%
-591
14.2%
1153
 
3.7%
3136
 
3.3%
6132
 
3.2%
5124
 
3.0%
4121
 
2.9%
865
 
1.6%
Other values (2)67
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4153
100.0%

Most frequent character per block

ValueCountFrequency (%)
01319
31.8%
2747
18.0%
7698
16.8%
-591
14.2%
1153
 
3.7%
3136
 
3.3%
6132
 
3.2%
5124
 
3.0%
4121
 
2.9%
865
 
1.6%
Other values (2)67
 
1.6%

date
Date

Distinct175
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Minimum2013-06-15 00:00:00
Maximum2013-12-24 00:00:00
2021-02-12T10:50:10.897899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:50:11.014180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

abdominal_pain_level
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
567 
1.0
 
18
3.0
 
4
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan567
95.9%
1.018
 
3.0%
3.04
 
0.7%
2.02
 
0.3%
2021-02-12T10:50:11.205765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:11.259508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan567
95.9%
1.018
 
3.0%
3.04
 
0.7%
2.02
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n1134
64.0%
a567
32.0%
.24
 
1.4%
024
 
1.4%
118
 
1.0%
34
 
0.2%
22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1701
95.9%
Decimal Number48
 
2.7%
Other Punctuation24
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
024
50.0%
118
37.5%
34
 
8.3%
22
 
4.2%
ValueCountFrequency (%)
n1134
66.7%
a567
33.3%
ValueCountFrequency (%)
.24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1701
95.9%
Common72
 
4.1%

Most frequent character per script

ValueCountFrequency (%)
.24
33.3%
024
33.3%
118
25.0%
34
 
5.6%
22
 
2.8%
ValueCountFrequency (%)
n1134
66.7%
a567
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1134
64.0%
a567
32.0%
.24
 
1.4%
024
 
1.4%
118
 
1.0%
34
 
0.2%
22
 
0.1%

abdominal_tenderness
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing266
Missing (%)45.0%
Memory size4.7 KiB
False
291 
True
34 
(Missing)
266 
ValueCountFrequency (%)
False291
49.2%
True34
 
5.8%
(Missing)266
45.0%
2021-02-12T10:50:11.305446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

age
Real number (ℝ≥0)

Distinct37
Distinct (%)6.3%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean29.98639456
Minimum5
Maximum64
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:11.374658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q120
median26
Q336
95-th percentile56
Maximum64
Range59
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.59388398
Coefficient of variation (CV)0.4533350602
Kurtosis-0.16922626
Mean29.98639456
Median Absolute Deviation (MAD)7
Skewness0.7861039106
Sum17632
Variance184.7936817
MonotocityNot monotonic
2021-02-12T10:50:11.483431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1951
 
8.6%
2434
 
5.8%
2932
 
5.4%
2232
 
5.4%
5329
 
4.9%
3127
 
4.6%
2627
 
4.6%
2326
 
4.4%
3425
 
4.2%
3624
 
4.1%
Other values (27)281
47.5%
ValueCountFrequency (%)
57
1.2%
99
1.5%
1112
2.0%
127
1.2%
1410
1.7%
ValueCountFrequency (%)
649
1.5%
5910
1.7%
589
1.5%
5611
1.9%
557
1.2%

albumin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct21
Distinct (%)32.8%
Missing527
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean40.96875
Minimum24
Maximum51
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:11.592644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile28
Q137.75
median42
Q346
95-th percentile50
Maximum51
Range27
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation6.480664159
Coefficient of variation (CV)0.1581855477
Kurtosis0.3350358426
Mean40.96875
Median Absolute Deviation (MAD)4
Skewness-0.8170171315
Sum2622
Variance41.99900794
MonotocityNot monotonic
2021-02-12T10:50:11.692715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
427
 
1.2%
476
 
1.0%
396
 
1.0%
435
 
0.8%
455
 
0.8%
464
 
0.7%
504
 
0.7%
483
 
0.5%
363
 
0.5%
343
 
0.5%
Other values (11)18
 
3.0%
(Missing)527
89.2%
ValueCountFrequency (%)
241
0.2%
252
0.3%
282
0.3%
311
0.2%
321
0.2%
ValueCountFrequency (%)
511
 
0.2%
504
0.7%
483
0.5%
476
1.0%
464
0.7%

alt
Real number (ℝ≥0)

MISSING

Distinct54
Distinct (%)66.7%
Missing510
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean61.35802469
Minimum8
Maximum437
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:11.798903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q129
median38
Q372
95-th percentile172
Maximum437
Range429
Interquartile range (IQR)43

Descriptive statistics

Standard deviation64.35221609
Coefficient of variation (CV)1.048798693
Kurtosis15.45445046
Mean61.35802469
Median Absolute Deviation (MAD)18
Skewness3.428885737
Sum4970
Variance4141.207716
MonotocityNot monotonic
2021-02-12T10:50:11.941391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
345
 
0.8%
204
 
0.7%
304
 
0.7%
384
 
0.7%
294
 
0.7%
173
 
0.5%
692
 
0.3%
472
 
0.3%
262
 
0.3%
122
 
0.3%
Other values (44)49
 
8.3%
(Missing)510
86.3%
ValueCountFrequency (%)
81
 
0.2%
122
0.3%
131
 
0.2%
151
 
0.2%
173
0.5%
ValueCountFrequency (%)
4371
0.2%
2891
0.2%
1981
0.2%
1801
0.2%
1721
0.2%

anemia
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
575 
True
 
16
ValueCountFrequency (%)
False575
97.3%
True16
 
2.7%
2021-02-12T10:50:12.018125image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

anorexia
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
True
516 
False
75 
ValueCountFrequency (%)
True516
87.3%
False75
 
12.7%
2021-02-12T10:50:12.060907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

antibiotics
Boolean

MISSING

Distinct2
Distinct (%)0.8%
Missing337
Missing (%)57.0%
Memory size4.7 KiB
False
212 
True
42 
(Missing)
337 
ValueCountFrequency (%)
False212
35.9%
True42
 
7.1%
(Missing)337
57.0%
2021-02-12T10:50:12.103734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

antibiotics_list
Categorical

MISSING

Distinct17
Distinct (%)40.5%
Missing549
Missing (%)92.9%
Memory size4.7 KiB
AZITHROMYCIN
CEFOPERAZONE+ SUNBACTAM
AMPICILIN+SULBACTAM
CEFTAZIDIME
ROCEPHIN
Other values (12)
20 

Length

Max length36
Median length12
Mean length16.11904762
Min length8

Characters and Unicode

Total characters677
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)16.7%

Sample

1st rowCEFTRYAXON, AZITHROMYCIN,DOXYCYCLIN
2nd rowCEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN
3rd rowCEFTRIAXON, AZITHROMYCIN, DOXYCYLIN
4th rowCEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN
5th rowCEFTRIAXON
ValueCountFrequency (%)
AZITHROMYCIN6
 
1.0%
CEFOPERAZONE+ SUNBACTAM4
 
0.7%
AMPICILIN+SULBACTAM4
 
0.7%
CEFTAZIDIME4
 
0.7%
ROCEPHIN4
 
0.7%
SAVIAZIT3
 
0.5%
CEFTRIAXONE, DOXYCYCLIN3
 
0.5%
AZITROMYCIN3
 
0.5%
CEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN2
 
0.3%
ERTAPENEM2
 
0.3%
Other values (7)7
 
1.2%
(Missing)549
92.9%
2021-02-12T10:50:12.291034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
azithromycin9
15.5%
doxycyclin6
10.3%
sunbactam4
 
6.9%
ampicilin+sulbactam4
 
6.9%
cefoperazone4
 
6.9%
rocephin4
 
6.9%
ceftazidime4
 
6.9%
ceftriaxon4
 
6.9%
azitromycin3
 
5.2%
ceftriaxone3
 
5.2%
Other values (10)13
22.4%

Most occurring characters

ValueCountFrequency (%)
I75
 
11.1%
C63
 
9.3%
A62
 
9.2%
N49
 
7.2%
E47
 
6.9%
O45
 
6.6%
T40
 
5.9%
R34
 
5.0%
M33
 
4.9%
Y30
 
4.4%
Other values (14)199
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter640
94.5%
Space Separator16
 
2.4%
Other Punctuation13
 
1.9%
Math Symbol8
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
I75
11.7%
C63
 
9.8%
A62
 
9.7%
N49
 
7.7%
E47
 
7.3%
O45
 
7.0%
T40
 
6.2%
R34
 
5.3%
M33
 
5.2%
Y30
 
4.7%
Other values (11)162
25.3%
ValueCountFrequency (%)
,13
100.0%
ValueCountFrequency (%)
16
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin640
94.5%
Common37
 
5.5%

Most frequent character per script

ValueCountFrequency (%)
I75
11.7%
C63
 
9.8%
A62
 
9.7%
N49
 
7.7%
E47
 
7.3%
O45
 
7.0%
T40
 
6.2%
R34
 
5.3%
M33
 
5.2%
Y30
 
4.7%
Other values (11)162
25.3%
ValueCountFrequency (%)
16
43.2%
,13
35.1%
+8
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII677
100.0%

Most frequent character per block

ValueCountFrequency (%)
I75
 
11.1%
C63
 
9.3%
A62
 
9.2%
N49
 
7.2%
E47
 
6.9%
O45
 
6.6%
T40
 
5.9%
R34
 
5.0%
M33
 
4.9%
Y30
 
4.4%
Other values (14)199
29.4%

ascites
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
553 
True
 
35
(Missing)
 
3
ValueCountFrequency (%)
False553
93.6%
True35
 
5.9%
(Missing)3
 
0.5%
2021-02-12T10:50:12.354828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ast
Real number (ℝ≥0)

MISSING

Distinct58
Distinct (%)71.6%
Missing510
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean85.7654321
Minimum17
Maximum831
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:12.440626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20
Q126
median42
Q376
95-th percentile352
Maximum831
Range814
Interquartile range (IQR)50

Descriptive statistics

Standard deviation129.1645531
Coefficient of variation (CV)1.506021132
Kurtosis17.30934205
Mean85.7654321
Median Absolute Deviation (MAD)19
Skewness3.887441517
Sum6947
Variance16683.48179
MonotocityNot monotonic
2021-02-12T10:50:12.584472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265
 
0.8%
573
 
0.5%
323
 
0.5%
243
 
0.5%
252
 
0.3%
222
 
0.3%
462
 
0.3%
342
 
0.3%
212
 
0.3%
292
 
0.3%
Other values (48)55
 
9.3%
(Missing)510
86.3%
ValueCountFrequency (%)
171
0.2%
181
0.2%
191
0.2%
202
0.3%
212
0.3%
ValueCountFrequency (%)
8311
0.2%
6301
0.2%
4081
0.2%
3901
0.2%
3521
0.2%

asthma
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
574 
True
 
17
ValueCountFrequency (%)
False574
97.1%
True17
 
2.9%
2021-02-12T10:50:12.657556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding
Boolean

MISSING

Distinct2
Distinct (%)0.7%
Missing302
Missing (%)51.1%
Memory size4.7 KiB
True
212 
False
77 
(Missing)
302 
ValueCountFrequency (%)
True212
35.9%
False77
 
13.0%
(Missing)302
51.1%
2021-02-12T10:50:12.695858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
549 
True
 
39
(Missing)
 
3
ValueCountFrequency (%)
False549
92.9%
True39
 
6.6%
(Missing)3
 
0.5%
2021-02-12T10:50:12.734328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
574 
True
 
14
(Missing)
 
3
ValueCountFrequency (%)
False574
97.1%
True14
 
2.4%
(Missing)3
 
0.5%
2021-02-12T10:50:12.769559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_vaginal
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing8
Missing (%)1.4%
Memory size4.7 KiB
False
547 
True
 
36
(Missing)
 
8
ValueCountFrequency (%)
False547
92.6%
True36
 
6.1%
(Missing)8
 
1.4%
2021-02-12T10:50:12.807256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

blood_fluid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
146 
True
 
1
(Missing)
444 
ValueCountFrequency (%)
False146
 
24.7%
True1
 
0.2%
(Missing)444
75.1%
2021-02-12T10:50:12.844209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

body_temperature
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)13.8%
Missing265
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean37.31717791
Minimum35.3
Maximum40
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:12.922811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum35.3
5-th percentile36
Q136.5
median37
Q338.2
95-th percentile39.5
Maximum40
Range4.7
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.128789418
Coefficient of variation (CV)0.03024852041
Kurtosis-0.6945074977
Mean37.31717791
Median Absolute Deviation (MAD)0.7
Skewness0.676671306
Sum12165.4
Variance1.27416555
MonotocityNot monotonic
2021-02-12T10:50:13.056121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36.538
 
6.4%
3728
 
4.7%
36.825
 
4.2%
3920
 
3.4%
3619
 
3.2%
36.217
 
2.9%
36.413
 
2.2%
39.512
 
2.0%
3811
 
1.9%
36.711
 
1.9%
Other values (35)132
22.3%
(Missing)265
44.8%
ValueCountFrequency (%)
35.32
 
0.3%
35.51
 
0.2%
35.61
 
0.2%
35.85
 
0.8%
3619
3.2%
ValueCountFrequency (%)
402
0.3%
39.91
 
0.2%
39.84
0.7%
39.71
 
0.2%
39.62
0.3%

bruising
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
True
313 
False
275 
(Missing)
 
3
ValueCountFrequency (%)
True313
53.0%
False275
46.5%
(Missing)3
 
0.5%
2021-02-12T10:50:13.129028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
580 
True
 
11
ValueCountFrequency (%)
False580
98.1%
True11
 
1.9%
2021-02-12T10:50:13.166537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

colloid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing445
Missing (%)75.3%
Memory size4.7 KiB
False
135 
True
 
11
(Missing)
445 
ValueCountFrequency (%)
False135
 
22.8%
True11
 
1.9%
(Missing)445
75.3%
2021-02-12T10:50:13.205088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

colloid_description
Categorical

MISSING

Distinct4
Distinct (%)40.0%
Missing581
Missing (%)98.3%
Memory size4.7 KiB
D60
ALBUMIN
REFORTAN 6%
D60, ALBUMIN

Length

Max length12
Median length5
Mean length5.9
Min length3

Characters and Unicode

Total characters59
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st rowALBUMIN
2nd rowALBUMIN
3rd rowALBUMIN
4th rowREFORTAN 6%
5th rowD60, ALBUMIN
ValueCountFrequency (%)
D605
 
0.8%
ALBUMIN3
 
0.5%
REFORTAN 6%1
 
0.2%
D60, ALBUMIN1
 
0.2%
(Missing)581
98.3%
2021-02-12T10:50:13.400871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:13.468135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
d606
50.0%
albumin4
33.3%
61
 
8.3%
refortan1
 
8.3%

Most occurring characters

ValueCountFrequency (%)
67
11.9%
D6
10.2%
06
10.2%
A5
8.5%
N5
8.5%
L4
 
6.8%
B4
 
6.8%
U4
 
6.8%
M4
 
6.8%
I4
 
6.8%
Other values (8)10
16.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter42
71.2%
Decimal Number13
 
22.0%
Space Separator2
 
3.4%
Other Punctuation2
 
3.4%

Most frequent character per category

ValueCountFrequency (%)
D6
14.3%
A5
11.9%
N5
11.9%
L4
9.5%
B4
9.5%
U4
9.5%
M4
9.5%
I4
9.5%
R2
 
4.8%
E1
 
2.4%
Other values (3)3
7.1%
ValueCountFrequency (%)
67
53.8%
06
46.2%
ValueCountFrequency (%)
%1
50.0%
,1
50.0%
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42
71.2%
Common17
28.8%

Most frequent character per script

ValueCountFrequency (%)
D6
14.3%
A5
11.9%
N5
11.9%
L4
9.5%
B4
9.5%
U4
9.5%
M4
9.5%
I4
9.5%
R2
 
4.8%
E1
 
2.4%
Other values (3)3
7.1%
ValueCountFrequency (%)
67
41.2%
06
35.3%
2
 
11.8%
%1
 
5.9%
,1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII59
100.0%

Most frequent character per block

ValueCountFrequency (%)
67
11.9%
D6
10.2%
06
10.2%
A5
8.5%
N5
8.5%
L4
 
6.8%
B4
 
6.8%
U4
 
6.8%
M4
 
6.8%
I4
 
6.8%
Other values (8)10
16.9%
Distinct2
Distinct (%)0.6%
Missing265
Missing (%)44.8%
Memory size4.7 KiB
False
263 
True
63 
(Missing)
265 
ValueCountFrequency (%)
False263
44.5%
True63
 
10.7%
(Missing)265
44.8%
2021-02-12T10:50:13.513998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)2.8%
Missing520
Missing (%)88.0%
Memory size4.7 KiB
False
70 
True
 
1
(Missing)
520 
ValueCountFrequency (%)
False70
 
11.8%
True1
 
0.2%
(Missing)520
88.0%
2021-02-12T10:50:13.552253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

creatine_kinase
Real number (ℝ≥0)

MISSING

Distinct33
Distinct (%)91.7%
Missing555
Missing (%)93.9%
Infinite0
Infinite (%)0.0%
Mean83.11111111
Minimum27
Maximum543
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:13.617756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile30
Q142.75
median57.5
Q375.75
95-th percentile199.25
Maximum543
Range516
Interquartile range (IQR)33

Descriptive statistics

Standard deviation104.4051936
Coefficient of variation (CV)1.256212222
Kurtosis14.63012228
Mean83.11111111
Median Absolute Deviation (MAD)17
Skewness3.848977529
Sum2992
Variance10900.44444
MonotocityNot monotonic
2021-02-12T10:50:13.729970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
272
 
0.3%
582
 
0.3%
352
 
0.3%
951
 
0.2%
471
 
0.2%
321
 
0.2%
421
 
0.2%
811
 
0.2%
751
 
0.2%
691
 
0.2%
Other values (23)23
 
3.9%
(Missing)555
93.9%
ValueCountFrequency (%)
272
0.3%
311
0.2%
321
0.2%
352
0.3%
371
0.2%
ValueCountFrequency (%)
5431
0.2%
4491
0.2%
1161
0.2%
1001
0.2%
951
0.2%

creatinine
Real number (ℝ≥0)

MISSING

Distinct47
Distinct (%)57.3%
Missing509
Missing (%)86.1%
Infinite0
Infinite (%)0.0%
Mean85.06097561
Minimum32
Maximum446
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:13.850839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile51.1
Q163
median74
Q389.25
95-th percentile112.9
Maximum446
Range414
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation59.23573904
Coefficient of variation (CV)0.696391484
Kurtosis29.51792775
Mean85.06097561
Median Absolute Deviation (MAD)12
Skewness5.21063045
Sum6975
Variance3508.872779
MonotocityNot monotonic
2021-02-12T10:50:13.980732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1065
 
0.8%
784
 
0.7%
684
 
0.7%
744
 
0.7%
533
 
0.5%
633
 
0.5%
753
 
0.5%
613
 
0.5%
802
 
0.3%
962
 
0.3%
Other values (37)49
 
8.3%
(Missing)509
86.1%
ValueCountFrequency (%)
321
0.2%
391
0.2%
471
0.2%
501
0.2%
511
0.2%
ValueCountFrequency (%)
4461
0.2%
4241
0.2%
1441
0.2%
1361
0.2%
1131
0.2%

crystalloid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
True
141 
False
 
6
(Missing)
444 
ValueCountFrequency (%)
True141
 
23.9%
False6
 
1.0%
(Missing)444
75.1%
2021-02-12T10:50:14.055186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

crystalloid_description
Categorical

MISSING

Distinct15
Distinct (%)10.6%
Missing450
Missing (%)76.1%
Memory size4.7 KiB
RL
99 
NS
14 
RL,NS
 
6
NS, GLUCOSE 5%
 
6
DEXTROSE-NATRI
 
4
Other values (10)
12 

Length

Max length15
Median length2
Mean length3.659574468
Min length2

Characters and Unicode

Total characters516
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)5.7%

Sample

1st rowNS
2nd rowNS
3rd rowRL
4th rowRL
5th rowRL
ValueCountFrequency (%)
RL99
 
16.8%
NS14
 
2.4%
RL,NS6
 
1.0%
NS, GLUCOSE 5%6
 
1.0%
DEXTROSE-NATRI4
 
0.7%
NS,RL2
 
0.3%
NS,GLUCOSE 5%2
 
0.3%
NS, RL1
 
0.2%
NS,GLUCOSE5%1
 
0.2%
NACL1
 
0.2%
Other values (5)5
 
0.8%
(Missing)450
76.1%
2021-02-12T10:50:14.413482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rl102
63.4%
ns21
 
13.0%
510
 
6.2%
glucose10
 
6.2%
rl,ns6
 
3.7%
dextrose-natri4
 
2.5%
ns,rl2
 
1.2%
ns,glucose2
 
1.2%
ns,glucose51
 
0.6%
ringer1
 
0.6%
Other values (2)2
 
1.2%

Most occurring characters

ValueCountFrequency (%)
L125
24.2%
R122
23.6%
S49
 
9.5%
N39
 
7.6%
E23
 
4.5%
,21
 
4.1%
20
 
3.9%
O17
 
3.3%
G15
 
2.9%
C15
 
2.9%
Other values (9)70
13.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter449
87.0%
Other Punctuation32
 
6.2%
Space Separator20
 
3.9%
Decimal Number11
 
2.1%
Dash Punctuation4
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
L125
27.8%
R122
27.2%
S49
 
10.9%
N39
 
8.7%
E23
 
5.1%
O17
 
3.8%
G15
 
3.3%
C15
 
3.3%
U13
 
2.9%
T10
 
2.2%
Other values (4)21
 
4.7%
ValueCountFrequency (%)
,21
65.6%
%11
34.4%
ValueCountFrequency (%)
20
100.0%
ValueCountFrequency (%)
511
100.0%
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin449
87.0%
Common67
 
13.0%

Most frequent character per script

ValueCountFrequency (%)
L125
27.8%
R122
27.2%
S49
 
10.9%
N39
 
8.7%
E23
 
5.1%
O17
 
3.8%
G15
 
3.3%
C15
 
3.3%
U13
 
2.9%
T10
 
2.2%
Other values (4)21
 
4.7%
ValueCountFrequency (%)
,21
31.3%
20
29.9%
511
16.4%
%11
16.4%
-4
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII516
100.0%

Most frequent character per block

ValueCountFrequency (%)
L125
24.2%
R122
23.6%
S49
 
9.5%
N39
 
7.6%
E23
 
4.5%
,21
 
4.1%
20
 
3.9%
O17
 
3.3%
G15
 
2.9%
C15
 
2.9%
Other values (9)70
13.6%

dbp
Real number (ℝ≥0)

MISSING

Distinct39
Distinct (%)14.3%
Missing319
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean67.98529412
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:14.546600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile52.55
Q160
median65
Q370
95-th percentile90
Maximum100
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.20324498
Coefficient of variation (CV)0.1647892404
Kurtosis1.012262986
Mean67.98529412
Median Absolute Deviation (MAD)5
Skewness1.079410189
Sum18492
Variance125.5126981
MonotocityNot monotonic
2021-02-12T10:50:14.666507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
6087
 
14.7%
7062
 
10.5%
8016
 
2.7%
6513
 
2.2%
5010
 
1.7%
1008
 
1.4%
626
 
1.0%
906
 
1.0%
824
 
0.7%
734
 
0.7%
Other values (29)56
 
9.5%
(Missing)319
54.0%
ValueCountFrequency (%)
5010
1.7%
511
 
0.2%
523
 
0.5%
531
 
0.2%
553
 
0.5%
ValueCountFrequency (%)
1008
1.4%
991
 
0.2%
982
 
0.3%
962
 
0.3%
906
1.0%

diabetes
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
576 
True
 
15
ValueCountFrequency (%)
False576
97.5%
True15
 
2.5%
2021-02-12T10:50:14.735651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

fluid_reason_other
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
134 
True
 
13
(Missing)
444 
ValueCountFrequency (%)
False134
 
22.7%
True13
 
2.2%
(Missing)444
75.1%
2021-02-12T10:50:14.775742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct7
Distinct (%)50.0%
Missing577
Missing (%)97.6%
Memory size4.7 KiB
PHASE TRANSFER OF ANTIBIOTIC
DIARRHEA
SICK PATIENTS
SOLVENT ANTIBIOTIC
DECREASED PLATELETS
Other values (2)

Length

Max length28
Median length18
Mean length17.28571429
Min length7

Characters and Unicode

Total characters242
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)21.4%

Sample

1st rowSOLVENT ANTIBIOTIC
2nd rowSOLVENT ANTIBIOTIC
3rd rowALBUMIN
4th rowDECREASED PLATELETS
5th rowPHASE TRANSFER OF ANTIBIOTIC
ValueCountFrequency (%)
PHASE TRANSFER OF ANTIBIOTIC4
 
0.7%
DIARRHEA3
 
0.5%
SICK PATIENTS2
 
0.3%
SOLVENT ANTIBIOTIC2
 
0.3%
DECREASED PLATELETS1
 
0.2%
TRADITIONAL FORTEC1
 
0.2%
ALBUMIN1
 
0.2%
(Missing)577
97.6%
2021-02-12T10:50:14.950267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:15.020707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
antibiotic6
18.8%
phase4
12.5%
transfer4
12.5%
of4
12.5%
diarrhea3
9.4%
sick2
 
6.2%
solvent2
 
6.2%
patients2
 
6.2%
fortec1
 
3.1%
decreased1
 
3.1%
Other values (3)3
9.4%

Most occurring characters

ValueCountFrequency (%)
I28
11.6%
T27
11.2%
A27
11.2%
E21
8.7%
18
 
7.4%
R17
 
7.0%
S16
 
6.6%
N16
 
6.6%
O14
 
5.8%
C10
 
4.1%
Other values (10)48
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter224
92.6%
Space Separator18
 
7.4%

Most frequent character per category

ValueCountFrequency (%)
I28
12.5%
T27
12.1%
A27
12.1%
E21
9.4%
R17
7.6%
S16
7.1%
N16
7.1%
O14
 
6.2%
C10
 
4.5%
F9
 
4.0%
Other values (9)39
17.4%
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin224
92.6%
Common18
 
7.4%

Most frequent character per script

ValueCountFrequency (%)
I28
12.5%
T27
12.1%
A27
12.1%
E21
9.4%
R17
7.6%
S16
7.1%
N16
7.1%
O14
 
6.2%
C10
 
4.5%
F9
 
4.0%
Other values (9)39
17.4%
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII242
100.0%

Most frequent character per block

ValueCountFrequency (%)
I28
11.6%
T27
11.2%
A27
11.2%
E21
8.7%
18
 
7.4%
R17
 
7.0%
S16
 
6.6%
N16
 
6.6%
O14
 
5.8%
C10
 
4.1%
Other values (10)48
19.8%

gender
Categorical

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
Male
311 
Female
277 

Length

Max length6
Median length4
Mean length4.942176871
Min length4

Characters and Unicode

Total characters2906
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
Male311
52.6%
Female277
46.9%
(Missing)3
 
0.5%
2021-02-12T10:50:15.208691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:15.268955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
male311
52.9%
female277
47.1%

Most occurring characters

ValueCountFrequency (%)
e865
29.8%
a588
20.2%
l588
20.2%
M311
 
10.7%
F277
 
9.5%
m277
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2318
79.8%
Uppercase Letter588
 
20.2%

Most frequent character per category

ValueCountFrequency (%)
e865
37.3%
a588
25.4%
l588
25.4%
m277
 
11.9%
ValueCountFrequency (%)
M311
52.9%
F277
47.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2906
100.0%

Most frequent character per script

ValueCountFrequency (%)
e865
29.8%
a588
20.2%
l588
20.2%
M311
 
10.7%
F277
 
9.5%
m277
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2906
100.0%

Most frequent character per block

ValueCountFrequency (%)
e865
29.8%
a588
20.2%
l588
20.2%
M311
 
10.7%
F277
 
9.5%
m277
 
9.5%

haematocrit_high
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
121 
True
 
26
(Missing)
444 
ValueCountFrequency (%)
False121
 
20.5%
True26
 
4.4%
(Missing)444
75.1%
2021-02-12T10:50:15.302809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

haematocrit_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct166
Distinct (%)49.6%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean40.53134328
Minimum13.85
Maximum54.5
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:15.382858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum13.85
5-th percentile33.14
Q137.3
median40.4
Q343.75
95-th percentile47.425
Maximum54.5
Range40.65
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation4.78043696
Coefficient of variation (CV)0.1179442025
Kurtosis2.313238949
Mean40.53134328
Median Absolute Deviation (MAD)3.2
Skewness-0.3381302229
Sum13578
Variance22.85257753
MonotocityNot monotonic
2021-02-12T10:50:15.497524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.18
 
1.4%
41.67
 
1.2%
39.86
 
1.0%
43.56
 
1.0%
42.45
 
0.8%
35.55
 
0.8%
425
 
0.8%
39.15
 
0.8%
40.75
 
0.8%
404
 
0.7%
Other values (156)279
47.2%
(Missing)256
43.3%
ValueCountFrequency (%)
13.851
0.2%
29.81
0.2%
30.61
0.2%
31.11
0.2%
31.31
0.2%
ValueCountFrequency (%)
54.51
0.2%
541
0.2%
53.11
0.2%
52.151
0.2%
51.61
0.2%

haemoglobin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct76
Distinct (%)23.3%
Missing265
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean13.7196319
Minimum9.3
Maximum18.5
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:15.616731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile10.9
Q112.4
median13.7
Q315
95-th percentile16.3
Maximum18.5
Range9.2
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.738110695
Coefficient of variation (CV)0.1266878519
Kurtosis-0.5064616875
Mean13.7196319
Median Absolute Deviation (MAD)1.3
Skewness-0.01823765764
Sum4472.6
Variance3.021028787
MonotocityNot monotonic
2021-02-12T10:50:15.747651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.912
 
2.0%
12.412
 
2.0%
12.911
 
1.9%
15.411
 
1.9%
13.210
 
1.7%
11.710
 
1.7%
14.810
 
1.7%
159
 
1.5%
11.98
 
1.4%
13.87
 
1.2%
Other values (66)226
38.2%
(Missing)265
44.8%
ValueCountFrequency (%)
9.31
 
0.2%
9.61
 
0.2%
101
 
0.2%
10.11
 
0.2%
10.23
0.5%
ValueCountFrequency (%)
18.51
0.2%
18.31
0.2%
17.62
0.3%
17.41
0.2%
17.21
0.2%

headache_level
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
527 
1.0
 
39
2.0
 
20
3.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan527
89.2%
1.039
 
6.6%
2.020
 
3.4%
3.05
 
0.8%
2021-02-12T10:50:15.943531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:16.008751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan527
89.2%
1.039
 
6.6%
2.020
 
3.4%
3.05
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n1054
59.4%
a527
29.7%
.64
 
3.6%
064
 
3.6%
139
 
2.2%
220
 
1.1%
35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1581
89.2%
Decimal Number128
 
7.2%
Other Punctuation64
 
3.6%

Most frequent character per category

ValueCountFrequency (%)
064
50.0%
139
30.5%
220
 
15.6%
35
 
3.9%
ValueCountFrequency (%)
n1054
66.7%
a527
33.3%
ValueCountFrequency (%)
.64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1581
89.2%
Common192
 
10.8%

Most frequent character per script

ValueCountFrequency (%)
.64
33.3%
064
33.3%
139
20.3%
220
 
10.4%
35
 
2.6%
ValueCountFrequency (%)
n1054
66.7%
a527
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1054
59.4%
a527
29.7%
.64
 
3.6%
064
 
3.6%
139
 
2.2%
220
 
1.1%
35
 
0.3%

height
Real number (ℝ≥0)

Distinct30
Distinct (%)5.1%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean160.5969388
Minimum110
Maximum180
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:16.088355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile145
Q1154
median162
Q3168
95-th percentile180
Maximum180
Range70
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.2440387
Coefficient of variation (CV)0.07001402882
Kurtosis3.820133846
Mean160.5969388
Median Absolute Deviation (MAD)7
Skewness-1.152379402
Sum94431
Variance126.4284063
MonotocityNot monotonic
2021-02-12T10:50:16.207998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
16059
 
10.0%
16854
 
9.1%
17046
 
7.8%
15042
 
7.1%
15235
 
5.9%
18032
 
5.4%
16329
 
4.9%
15528
 
4.7%
16226
 
4.4%
15820
 
3.4%
Other values (20)217
36.7%
ValueCountFrequency (%)
1107
1.2%
1309
1.5%
14517
2.9%
1467
1.2%
14712
2.0%
ValueCountFrequency (%)
18032
5.4%
1798
 
1.4%
1768
 
1.4%
1748
 
1.4%
1729
 
1.5%

hematemesis
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
326 
True
 
1
(Missing)
264 
ValueCountFrequency (%)
False326
55.2%
True1
 
0.2%
(Missing)264
44.7%
2021-02-12T10:50:16.277896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hypertension
Boolean

MISSING

Distinct2
Distinct (%)2.8%
Missing519
Missing (%)87.8%
Memory size4.7 KiB
False
69 
True
 
3
(Missing)
519 
ValueCountFrequency (%)
False69
 
11.7%
True3
 
0.5%
(Missing)519
87.8%
2021-02-12T10:50:16.315910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

igg
Real number (ℝ≥0)

MISSING

Distinct112
Distinct (%)87.5%
Missing463
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean17.35148438
Minimum0.78
Maximum28.21
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:16.400114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile1.23
Q17.2
median21.95
Q325.52
95-th percentile27.3855
Maximum28.21
Range27.43
Interquartile range (IQR)18.32

Descriptive statistics

Standard deviation9.52503978
Coefficient of variation (CV)0.5489466823
Kurtosis-1.163393544
Mean17.35148438
Median Absolute Deviation (MAD)4.57
Skewness-0.666192067
Sum2220.99
Variance90.72638282
MonotocityNot monotonic
2021-02-12T10:50:16.529578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.183
 
0.5%
27.083
 
0.5%
26.092
 
0.3%
222
 
0.3%
21.652
 
0.3%
22.882
 
0.3%
1.342
 
0.3%
272
 
0.3%
23.132
 
0.3%
1.232
 
0.3%
Other values (102)106
 
17.9%
(Missing)463
78.3%
ValueCountFrequency (%)
0.781
0.2%
0.791
0.2%
0.861
0.2%
1.031
0.2%
1.041
0.2%
ValueCountFrequency (%)
28.211
0.2%
28.121
0.2%
27.891
0.2%
27.881
0.2%
27.621
0.2%

igg_interpretation
Categorical

MISSING

Distinct3
Distinct (%)2.3%
Missing463
Missing (%)78.3%
Memory size4.7 KiB
Positive
64 
Negative
46 
Equivocal
18 

Length

Max length9
Median length8
Mean length8.140625
Min length8

Characters and Unicode

Total characters1042
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Positive64
 
10.8%
Negative46
 
7.8%
Equivocal18
 
3.0%
(Missing)463
78.3%
2021-02-12T10:50:16.720776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:16.774696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive64
50.0%
negative46
35.9%
equivocal18
 
14.1%

Most occurring characters

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter914
87.7%
Uppercase Letter128
 
12.3%

Most frequent character per category

ValueCountFrequency (%)
i192
21.0%
e156
17.1%
v128
14.0%
t110
12.0%
o82
9.0%
s64
 
7.0%
a64
 
7.0%
g46
 
5.0%
q18
 
2.0%
u18
 
2.0%
Other values (2)36
 
3.9%
ValueCountFrequency (%)
P64
50.0%
N46
35.9%
E18
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1042
100.0%

Most frequent character per script

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1042
100.0%

Most frequent character per block

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

igm
Real number (ℝ≥0)

MISSING

Distinct122
Distinct (%)95.3%
Missing463
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean25.87273438
Minimum5.27
Maximum45.71
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:16.862225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5.27
5-th percentile6.3895
Q113.5725
median26.81
Q337.3275
95-th percentile45.0995
Maximum45.71
Range40.44
Interquartile range (IQR)23.755

Descriptive statistics

Standard deviation13.44181229
Coefficient of variation (CV)0.5195358208
Kurtosis-1.425192115
Mean25.87273438
Median Absolute Deviation (MAD)12.12
Skewness-0.05696820729
Sum3311.71
Variance180.6823177
MonotocityNot monotonic
2021-02-12T10:50:16.990579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.12
 
0.3%
26.812
 
0.3%
29.242
 
0.3%
45.682
 
0.3%
36.262
 
0.3%
45.082
 
0.3%
24.991
 
0.2%
25.461
 
0.2%
44.911
 
0.2%
12.031
 
0.2%
Other values (112)112
 
19.0%
(Missing)463
78.3%
ValueCountFrequency (%)
5.271
0.2%
5.561
0.2%
5.731
0.2%
6.12
0.3%
6.161
0.2%
ValueCountFrequency (%)
45.711
0.2%
45.682
0.3%
45.511
0.2%
45.341
0.2%
45.281
0.2%

igm_interpretation
Categorical

MISSING

Distinct3
Distinct (%)2.3%
Missing463
Missing (%)78.3%
Memory size4.7 KiB
Positive
103 
Negative
21 
Equivocal
 
4

Length

Max length9
Median length8
Mean length8.03125
Min length8

Characters and Unicode

Total characters1028
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowEquivocal
5th rowNegative
ValueCountFrequency (%)
Positive103
 
17.4%
Negative21
 
3.6%
Equivocal4
 
0.7%
(Missing)463
78.3%
2021-02-12T10:50:17.202551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:17.261593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive103
80.5%
negative21
 
16.4%
equivocal4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter900
87.5%
Uppercase Letter128
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
i231
25.7%
e145
16.1%
v128
14.2%
t124
13.8%
o107
11.9%
s103
11.4%
a25
 
2.8%
g21
 
2.3%
q4
 
0.4%
u4
 
0.4%
Other values (2)8
 
0.9%
ValueCountFrequency (%)
P103
80.5%
N21
 
16.4%
E4
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1028
100.0%

Most frequent character per script

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028
100.0%

Most frequent character per block

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

joint_pain_level
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
549 
1.0
 
32
2.0
 
8
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan549
92.9%
1.032
 
5.4%
2.08
 
1.4%
3.02
 
0.3%
2021-02-12T10:50:17.436826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:17.494306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan549
92.9%
1.032
 
5.4%
2.08
 
1.4%
3.02
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n1098
61.9%
a549
31.0%
.42
 
2.4%
042
 
2.4%
132
 
1.8%
28
 
0.5%
32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1647
92.9%
Decimal Number84
 
4.7%
Other Punctuation42
 
2.4%

Most frequent character per category

ValueCountFrequency (%)
042
50.0%
132
38.1%
28
 
9.5%
32
 
2.4%
ValueCountFrequency (%)
n1098
66.7%
a549
33.3%
ValueCountFrequency (%)
.42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1647
92.9%
Common126
 
7.1%

Most frequent character per script

ValueCountFrequency (%)
.42
33.3%
042
33.3%
132
25.4%
28
 
6.3%
32
 
1.6%
ValueCountFrequency (%)
n1098
66.7%
a549
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1098
61.9%
a549
31.0%
.42
 
2.4%
042
 
2.4%
132
 
1.8%
28
 
0.5%
32
 
0.1%

lethargy
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
323 
True
 
4
(Missing)
264 
ValueCountFrequency (%)
False323
54.7%
True4
 
0.7%
(Missing)264
44.7%
2021-02-12T10:50:17.538765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_palpation
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing265
Missing (%)44.8%
Memory size4.7 KiB
False
310 
True
 
16
(Missing)
265 
ValueCountFrequency (%)
False310
52.5%
True16
 
2.7%
(Missing)265
44.8%
2021-02-12T10:50:17.577950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_size
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
575 
2.0
 
13
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan575
97.3%
2.013
 
2.2%
1.03
 
0.5%
2021-02-12T10:50:17.746926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:17.801991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan575
97.3%
2.013
 
2.2%
1.03
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n1150
64.9%
a575
32.4%
.16
 
0.9%
016
 
0.9%
213
 
0.7%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1725
97.3%
Decimal Number32
 
1.8%
Other Punctuation16
 
0.9%

Most frequent character per category

ValueCountFrequency (%)
016
50.0%
213
40.6%
13
 
9.4%
ValueCountFrequency (%)
n1150
66.7%
a575
33.3%
ValueCountFrequency (%)
.16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1725
97.3%
Common48
 
2.7%

Most frequent character per script

ValueCountFrequency (%)
.16
33.3%
016
33.3%
213
27.1%
13
 
6.2%
ValueCountFrequency (%)
n1150
66.7%
a575
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1150
64.9%
a575
32.4%
.16
 
0.9%
016
 
0.9%
213
 
0.7%
13
 
0.2%

lymphadenopathy
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing266
Missing (%)45.0%
Memory size4.7 KiB
False
300 
True
 
25
(Missing)
266 
ValueCountFrequency (%)
False300
50.8%
True25
 
4.2%
(Missing)266
45.0%
2021-02-12T10:50:17.842952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)8.0%
Missing566
Missing (%)95.8%
Memory size4.7 KiB
1.0
18 
Cervical

Length

Max length8
Median length3
Mean length4.4
Min length3

Characters and Unicode

Total characters110
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCervical
2nd row1.0
3rd row1.0
4th rowCervical
5th row1.0
ValueCountFrequency (%)
1.018
 
3.0%
Cervical7
 
1.2%
(Missing)566
95.8%
2021-02-12T10:50:18.010122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:18.067122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.018
72.0%
cervical7
 
28.0%

Most occurring characters

ValueCountFrequency (%)
118
16.4%
.18
16.4%
018
16.4%
C7
 
6.4%
e7
 
6.4%
r7
 
6.4%
v7
 
6.4%
i7
 
6.4%
c7
 
6.4%
a7
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter49
44.5%
Decimal Number36
32.7%
Other Punctuation18
 
16.4%
Uppercase Letter7
 
6.4%

Most frequent character per category

ValueCountFrequency (%)
e7
14.3%
r7
14.3%
v7
14.3%
i7
14.3%
c7
14.3%
a7
14.3%
l7
14.3%
ValueCountFrequency (%)
118
50.0%
018
50.0%
ValueCountFrequency (%)
C7
100.0%
ValueCountFrequency (%)
.18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin56
50.9%
Common54
49.1%

Most frequent character per script

ValueCountFrequency (%)
C7
12.5%
e7
12.5%
r7
12.5%
v7
12.5%
i7
12.5%
c7
12.5%
a7
12.5%
l7
12.5%
ValueCountFrequency (%)
118
33.3%
.18
33.3%
018
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110
100.0%

Most frequent character per block

ValueCountFrequency (%)
118
16.4%
.18
16.4%
018
16.4%
C7
 
6.4%
e7
 
6.4%
r7
 
6.4%
v7
 
6.4%
i7
 
6.4%
c7
 
6.4%
a7
 
6.4%

lymphocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct255
Distinct (%)76.1%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean40.9880597
Minimum4.1
Maximum77.7
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:18.157132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile14.67
Q129.15
median41.5
Q351.9
95-th percentile65.9
Maximum77.7
Range73.6
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation15.29407073
Coefficient of variation (CV)0.3731347822
Kurtosis-0.6521699417
Mean40.9880597
Median Absolute Deviation (MAD)11.4
Skewness-0.04490356914
Sum13731
Variance233.9085995
MonotocityNot monotonic
2021-02-12T10:50:18.293147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.74
 
0.7%
19.54
 
0.7%
383
 
0.5%
49.93
 
0.5%
323
 
0.5%
34.83
 
0.5%
43.13
 
0.5%
23.33
 
0.5%
52.53
 
0.5%
56.73
 
0.5%
Other values (245)303
51.3%
(Missing)256
43.3%
ValueCountFrequency (%)
4.11
0.2%
4.81
0.2%
6.41
0.2%
9.41
0.2%
101
0.2%
ValueCountFrequency (%)
77.71
0.2%
71.51
0.2%
70.52
0.3%
70.31
0.2%
70.21
0.2%

medication
Boolean

MISSING

Distinct2
Distinct (%)0.8%
Missing338
Missing (%)57.2%
Memory size4.7 KiB
True
135 
False
118 
(Missing)
338 
ValueCountFrequency (%)
True135
 
22.8%
False118
 
20.0%
(Missing)338
57.2%
2021-02-12T10:50:18.381407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

medication_list
Categorical

HIGH CARDINALITY
MISSING

Distinct90
Distinct (%)65.7%
Missing454
Missing (%)76.8%
Memory size4.7 KiB
A
24 
H: PANTOPRAZOL
 
5
A,H: SMECTA
 
4
F
 
3
H;LORATADIN
 
3
Other values (85)
98 

Length

Max length55
Median length14
Mean length16.15328467
Min length1

Characters and Unicode

Total characters2213
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)53.3%

Sample

1st rowH; PIRACETAM, CIGENOL,VITAMIN C
2nd rowA, H:PIRACETAM
3rd rowA, H: PIRACETAM
4th rowH: PIRACETAM, OMEPRAZOL
5th rowH: PIRACETAM, OMEPRAZOLE
ValueCountFrequency (%)
A24
 
4.1%
H: PANTOPRAZOL5
 
0.8%
A,H: SMECTA4
 
0.7%
F3
 
0.5%
H;LORATADIN3
 
0.5%
H:GASTROLIUM, PANTOPRAZOLE,ACID TRANEXAMID3
 
0.5%
H: DIPHENHYDRAMIN HYDROCLORID2
 
0.3%
D2
 
0.3%
H: CIGENOL2
 
0.3%
H: DOXYCYCLIN,2
 
0.3%
Other values (80)87
 
14.7%
(Missing)454
76.8%
2021-02-12T10:50:18.608083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h40
 
15.7%
a32
 
12.5%
a,h11
 
4.3%
pantoprazol9
 
3.5%
piracetam5
 
2.0%
smecta5
 
2.0%
d5
 
2.0%
omeprazole5
 
2.0%
omeprazol4
 
1.6%
pantoprazole,acid4
 
1.6%
Other values (76)135
52.9%

Most occurring characters

ValueCountFrequency (%)
A290
 
13.1%
O167
 
7.5%
I137
 
6.2%
E134
 
6.1%
R126
 
5.7%
119
 
5.4%
T117
 
5.3%
H116
 
5.2%
L111
 
5.0%
N104
 
4.7%
Other values (22)792
35.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1894
85.6%
Other Punctuation197
 
8.9%
Space Separator119
 
5.4%
Decimal Number2
 
0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A290
15.3%
O167
 
8.8%
I137
 
7.2%
E134
 
7.1%
R126
 
6.7%
T117
 
6.2%
H116
 
6.1%
L111
 
5.9%
N104
 
5.5%
M99
 
5.2%
Other values (14)493
26.0%
ValueCountFrequency (%)
,102
51.8%
:75
38.1%
;18
 
9.1%
.2
 
1.0%
ValueCountFrequency (%)
11
50.0%
01
50.0%
ValueCountFrequency (%)
119
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1894
85.6%
Common319
 
14.4%

Most frequent character per script

ValueCountFrequency (%)
A290
15.3%
O167
 
8.8%
I137
 
7.2%
E134
 
7.1%
R126
 
6.7%
T117
 
6.2%
H116
 
6.1%
L111
 
5.9%
N104
 
5.5%
M99
 
5.2%
Other values (14)493
26.0%
ValueCountFrequency (%)
119
37.3%
,102
32.0%
:75
23.5%
;18
 
5.6%
.2
 
0.6%
-1
 
0.3%
11
 
0.3%
01
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2213
100.0%

Most frequent character per block

ValueCountFrequency (%)
A290
 
13.1%
O167
 
7.5%
I137
 
6.2%
E134
 
6.1%
R126
 
5.7%
119
 
5.4%
T117
 
5.3%
H116
 
5.2%
L111
 
5.0%
N104
 
4.7%
Other values (22)792
35.8%

melaena
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
326 
True
 
1
(Missing)
264 
ValueCountFrequency (%)
False326
55.2%
True1
 
0.2%
(Missing)264
44.7%
2021-02-12T10:50:18.679686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

monocytes_percent
Real number (ℝ≥0)

MISSING

Distinct179
Distinct (%)53.4%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean16.49791045
Minimum2.4
Maximum60.9
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:18.761751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile6.64
Q110.7
median14.9
Q319.05
95-th percentile33.67
Maximum60.9
Range58.5
Interquartile range (IQR)8.35

Descriptive statistics

Standard deviation8.829551724
Coefficient of variation (CV)0.5351921234
Kurtosis5.174629379
Mean16.49791045
Median Absolute Deviation (MAD)4.2
Skewness1.941601728
Sum5526.8
Variance77.96098364
MonotocityNot monotonic
2021-02-12T10:50:18.878112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.66
 
1.0%
135
 
0.8%
14.85
 
0.8%
11.95
 
0.8%
13.95
 
0.8%
11.84
 
0.7%
9.44
 
0.7%
9.24
 
0.7%
11.24
 
0.7%
15.34
 
0.7%
Other values (169)289
48.9%
(Missing)256
43.3%
ValueCountFrequency (%)
2.41
0.2%
2.61
0.2%
4.61
0.2%
4.81
0.2%
5.41
0.2%
ValueCountFrequency (%)
60.91
0.2%
53.31
0.2%
52.31
0.2%
51.22
0.3%
47.91
0.2%
Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
527 
1.0
 
50
2.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan527
89.2%
1.050
 
8.5%
2.014
 
2.4%
2021-02-12T10:50:19.083828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:19.137309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan527
89.2%
1.050
 
8.5%
2.014
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n1054
59.4%
a527
29.7%
.64
 
3.6%
064
 
3.6%
150
 
2.8%
214
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1581
89.2%
Decimal Number128
 
7.2%
Other Punctuation64
 
3.6%

Most frequent character per category

ValueCountFrequency (%)
064
50.0%
150
39.1%
214
 
10.9%
ValueCountFrequency (%)
n1054
66.7%
a527
33.3%
ValueCountFrequency (%)
.64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1581
89.2%
Common192
 
10.8%

Most frequent character per script

ValueCountFrequency (%)
.64
33.3%
064
33.3%
150
26.0%
214
 
7.3%
ValueCountFrequency (%)
n1054
66.7%
a527
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1054
59.4%
a527
29.7%
.64
 
3.6%
064
 
3.6%
150
 
2.8%
214
 
0.8%

neutrophils_percent
Real number (ℝ≥0)

MISSING

Distinct255
Distinct (%)76.3%
Missing257
Missing (%)43.5%
Infinite0
Infinite (%)0.0%
Mean40.20688623
Minimum10.5
Maximum90.2
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:19.228430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile15.965
Q126.875
median38.5
Q351.075
95-th percentile71.87
Maximum90.2
Range79.7
Interquartile range (IQR)24.2

Descriptive statistics

Standard deviation17.20698704
Coefficient of variation (CV)0.427961194
Kurtosis-0.4490802495
Mean40.20688623
Median Absolute Deviation (MAD)12.35
Skewness0.4591597173
Sum13429.1
Variance296.0804029
MonotocityNot monotonic
2021-02-12T10:50:19.350308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.84
 
0.7%
25.53
 
0.5%
31.53
 
0.5%
25.13
 
0.5%
58.93
 
0.5%
213
 
0.5%
35.73
 
0.5%
55.63
 
0.5%
38.63
 
0.5%
46.13
 
0.5%
Other values (245)303
51.3%
(Missing)257
43.5%
ValueCountFrequency (%)
10.51
0.2%
10.71
0.2%
11.12
0.3%
11.62
0.3%
11.81
0.2%
ValueCountFrequency (%)
90.21
0.2%
86.51
0.2%
81.21
0.2%
79.52
0.3%
78.32
0.3%
Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
569 
True
 
19
(Missing)
 
3
ValueCountFrequency (%)
False569
96.3%
True19
 
3.2%
(Missing)3
 
0.5%
2021-02-12T10:50:19.429813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

parental_fluid_volume
Real number (ℝ≥0)

MISSING

Distinct59
Distinct (%)40.4%
Missing445
Missing (%)75.3%
Infinite0
Infinite (%)0.0%
Mean1095.291096
Minimum100
Maximum5850
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:19.512063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile332.25
Q1644.125
median1000
Q31000
95-th percentile2529.75
Maximum5850
Range5750
Interquartile range (IQR)355.875

Descriptive statistics

Standard deviation766.3389314
Coefficient of variation (CV)0.6996669052
Kurtosis11.53956114
Mean1095.291096
Median Absolute Deviation (MAD)120
Skewness2.80300881
Sum159912.5
Variance587275.3578
MonotocityNot monotonic
2021-02-12T10:50:19.633894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100061
 
10.3%
50020
 
3.4%
35003
 
0.5%
10602
 
0.3%
8202
 
0.3%
1002
 
0.3%
15002
 
0.3%
8802
 
0.3%
12502
 
0.3%
1871
 
0.2%
Other values (49)49
 
8.3%
(Missing)445
75.3%
ValueCountFrequency (%)
1002
0.3%
1501
0.2%
1871
0.2%
2001
0.2%
2501
0.2%
ValueCountFrequency (%)
58501
 
0.2%
36001
 
0.2%
35003
0.5%
30251
 
0.2%
28331
 
0.2%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Lab-confirmed Dengue
589 
Not Dengue
 
2

Length

Max length20
Median length20
Mean length19.96615905
Min length10

Characters and Unicode

Total characters11800
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLab-confirmed Dengue
2nd rowLab-confirmed Dengue
3rd rowLab-confirmed Dengue
4th rowLab-confirmed Dengue
5th rowLab-confirmed Dengue
ValueCountFrequency (%)
Lab-confirmed Dengue589
99.7%
Not Dengue2
 
0.3%
2021-02-12T10:50:19.840258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:19.898424image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
dengue591
50.0%
lab-confirmed589
49.8%
not2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e1771
15.0%
n1180
 
10.0%
o591
 
5.0%
591
 
5.0%
D591
 
5.0%
g591
 
5.0%
u591
 
5.0%
L589
 
5.0%
a589
 
5.0%
b589
 
5.0%
Other values (9)4127
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9438
80.0%
Uppercase Letter1182
 
10.0%
Space Separator591
 
5.0%
Dash Punctuation589
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
e1771
18.8%
n1180
12.5%
o591
 
6.3%
g591
 
6.3%
u591
 
6.3%
a589
 
6.2%
b589
 
6.2%
c589
 
6.2%
f589
 
6.2%
i589
 
6.2%
Other values (4)1769
18.7%
ValueCountFrequency (%)
D591
50.0%
L589
49.8%
N2
 
0.2%
ValueCountFrequency (%)
-589
100.0%
ValueCountFrequency (%)
591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10620
90.0%
Common1180
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
e1771
16.7%
n1180
11.1%
o591
 
5.6%
D591
 
5.6%
g591
 
5.6%
u591
 
5.6%
L589
 
5.5%
a589
 
5.5%
b589
 
5.5%
c589
 
5.5%
Other values (7)2949
27.8%
ValueCountFrequency (%)
591
50.1%
-589
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII11800
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1771
15.0%
n1180
 
10.0%
o591
 
5.0%
591
 
5.0%
D591
 
5.0%
g591
 
5.0%
u591
 
5.0%
L589
 
5.0%
a589
 
5.0%
b589
 
5.0%
Other values (9)4127
35.0%

pcr_dengue_load
Real number (ℝ≥0)

MISSING
ZEROS

Distinct52
Distinct (%)72.2%
Missing519
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean161132294.9
Minimum0
Maximum4080000000
Zeros21
Zeros (%)3.6%
Memory size4.7 KiB
2021-02-12T10:50:19.990076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median144500
Q37728571.75
95-th percentile595500000
Maximum4080000000
Range4080000000
Interquartile range (IQR)7728571.75

Descriptive statistics

Standard deviation678494348.1
Coefficient of variation (CV)4.210790572
Kurtosis28.62976761
Mean161132294.9
Median Absolute Deviation (MAD)144500
Skewness5.331735494
Sum1.160152523 × 1010
Variance4.603545804 × 1017
MonotocityNot monotonic
2021-02-12T10:50:20.120504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021
 
3.6%
439000001
 
0.2%
352001
 
0.2%
473000001
 
0.2%
43035711
 
0.2%
2770000001
 
0.2%
15800001
 
0.2%
74401
 
0.2%
53333331
 
0.2%
762001
 
0.2%
Other values (42)42
 
7.1%
(Missing)519
87.8%
ValueCountFrequency (%)
021
3.6%
17401
 
0.2%
30601
 
0.2%
35801
 
0.2%
47201
 
0.2%
ValueCountFrequency (%)
40800000001
0.2%
39300000001
0.2%
12559523811
0.2%
6010000001
0.2%
5910000001
0.2%

pcr_dengue_serotype
Categorical

MISSING

Distinct5
Distinct (%)0.9%
Missing24
Missing (%)4.1%
Memory size4.7 KiB
<LOD
160 
DENV-3
141 
DENV-4
105 
DENV-1
88 
DENV-2
73 

Length

Max length6
Median length6
Mean length5.435626102
Min length4

Characters and Unicode

Total characters3082
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<LOD
2nd row<LOD
3rd row<LOD
4th row<LOD
5th row<LOD
ValueCountFrequency (%)
<LOD160
27.1%
DENV-3141
23.9%
DENV-4105
17.8%
DENV-188
14.9%
DENV-273
12.4%
(Missing)24
 
4.1%
2021-02-12T10:50:20.354484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:20.429160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lod160
28.2%
denv-3141
24.9%
denv-4105
18.5%
denv-188
15.5%
denv-273
12.9%

Most occurring characters

ValueCountFrequency (%)
D567
18.4%
E407
13.2%
N407
13.2%
V407
13.2%
-407
13.2%
<160
 
5.2%
L160
 
5.2%
O160
 
5.2%
3141
 
4.6%
4105
 
3.4%
Other values (2)161
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
68.4%
Dash Punctuation407
 
13.2%
Decimal Number407
 
13.2%
Math Symbol160
 
5.2%

Most frequent character per category

ValueCountFrequency (%)
D567
26.9%
E407
19.3%
N407
19.3%
V407
19.3%
L160
 
7.6%
O160
 
7.6%
ValueCountFrequency (%)
3141
34.6%
4105
25.8%
188
21.6%
273
17.9%
ValueCountFrequency (%)
<160
100.0%
ValueCountFrequency (%)
-407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
68.4%
Common974
31.6%

Most frequent character per script

ValueCountFrequency (%)
-407
41.8%
<160
 
16.4%
3141
 
14.5%
4105
 
10.8%
188
 
9.0%
273
 
7.5%
ValueCountFrequency (%)
D567
26.9%
E407
19.3%
N407
19.3%
V407
19.3%
L160
 
7.6%
O160
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3082
100.0%

Most frequent character per block

ValueCountFrequency (%)
D567
18.4%
E407
13.2%
N407
13.2%
V407
13.2%
-407
13.2%
<160
 
5.2%
L160
 
5.2%
O160
 
5.2%
3141
 
4.6%
4105
 
3.4%
Other values (2)161
 
5.2%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
520 
True
71 
ValueCountFrequency (%)
False520
88.0%
True71
 
12.0%
2021-02-12T10:50:20.484120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

petechiae
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
459 
True
129 
(Missing)
 
3
ValueCountFrequency (%)
False459
77.7%
True129
 
21.8%
(Missing)3
 
0.5%
2021-02-12T10:50:20.518757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pharyngeal_injection
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing266
Missing (%)45.0%
Memory size4.7 KiB
False
285 
True
40 
(Missing)
266 
ValueCountFrequency (%)
False285
48.2%
True40
 
6.8%
(Missing)266
45.0%
2021-02-12T10:50:20.554630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

platelets
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
137 
True
 
10
(Missing)
444 
ValueCountFrequency (%)
False137
 
23.2%
True10
 
1.7%
(Missing)444
75.1%
2021-02-12T10:50:20.598931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
538 
True
 
50
(Missing)
 
3
ValueCountFrequency (%)
False538
91.0%
True50
 
8.5%
(Missing)3
 
0.5%
2021-02-12T10:50:20.633899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

plt
Real number (ℝ≥0)

MISSING

Distinct168
Distinct (%)50.1%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean103.6444776
Minimum9
Maximum687
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:20.713418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15.7
Q139.5
median72
Q3123
95-th percentile321.3
Maximum687
Range678
Interquartile range (IQR)83.5

Descriptive statistics

Standard deviation99.63932963
Coefficient of variation (CV)0.9613568608
Kurtosis6.474535472
Mean103.6444776
Median Absolute Deviation (MAD)40
Skewness2.272372525
Sum34720.9
Variance9927.99601
MonotocityNot monotonic
2021-02-12T10:50:20.856115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
227
 
1.2%
637
 
1.2%
847
 
1.2%
306
 
1.0%
586
 
1.0%
696
 
1.0%
885
 
0.8%
175
 
0.8%
244
 
0.7%
374
 
0.7%
Other values (158)278
47.0%
(Missing)256
43.3%
ValueCountFrequency (%)
92
0.3%
102
0.3%
112
0.3%
123
0.5%
13.91
 
0.2%
ValueCountFrequency (%)
6871
0.2%
5321
0.2%
5032
0.3%
4391
0.2%
4332
0.3%

pregnant
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
580 
True
 
11
ValueCountFrequency (%)
False580
98.1%
True11
 
1.9%
2021-02-12T10:50:20.939695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pulse
Real number (ℝ≥0)

MISSING

Distinct55
Distinct (%)17.1%
Missing269
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean80.13043478
Minimum52
Maximum125
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:21.034539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile60.1
Q172
median80
Q388
95-th percentile100
Maximum125
Range73
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.17685381
Coefficient of variation (CV)0.151962907
Kurtosis0.2604565193
Mean80.13043478
Median Absolute Deviation (MAD)8
Skewness0.4032746933
Sum25802
Variance148.2757687
MonotocityNot monotonic
2021-02-12T10:50:21.167623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8034
 
5.8%
7024
 
4.1%
7518
 
3.0%
8816
 
2.7%
9013
 
2.2%
7813
 
2.2%
7412
 
2.0%
8512
 
2.0%
659
 
1.5%
848
 
1.4%
Other values (45)163
27.6%
(Missing)269
45.5%
ValueCountFrequency (%)
521
 
0.2%
551
 
0.2%
561
 
0.2%
573
0.5%
582
0.3%
ValueCountFrequency (%)
1251
0.2%
1181
0.2%
1141
0.2%
1102
0.3%
1091
0.2%

rales_crackles
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
322 
True
 
5
(Missing)
264 
ValueCountFrequency (%)
False322
54.5%
True5
 
0.8%
(Missing)264
44.7%
2021-02-12T10:50:21.238062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

rehydration
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
True
135 
False
 
12
(Missing)
444 
ValueCountFrequency (%)
True135
 
22.8%
False12
 
2.0%
(Missing)444
75.1%
2021-02-12T10:50:21.279210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

renal_disease
Boolean

MISSING

Distinct2
Distinct (%)2.8%
Missing519
Missing (%)87.8%
Memory size4.7 KiB
False
71 
True
 
1
(Missing)
519 
ValueCountFrequency (%)
False71
 
12.0%
True1
 
0.2%
(Missing)519
87.8%
2021-02-12T10:50:21.314599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
580 
True
 
8
(Missing)
 
3
ValueCountFrequency (%)
False580
98.1%
True8
 
1.4%
(Missing)3
 
0.5%
2021-02-12T10:50:21.351563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

respiratory_rate
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)4.0%
Missing266
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean19.65846154
Minimum15
Maximum28
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:21.422371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q118
median20
Q320
95-th percentile23
Maximum28
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.797708788
Coefficient of variation (CV)0.09144707403
Kurtosis2.232740174
Mean19.65846154
Median Absolute Deviation (MAD)1
Skewness1.104787419
Sum6389
Variance3.231756885
MonotocityNot monotonic
2021-02-12T10:50:21.530179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20137
23.2%
18104
 
17.6%
2224
 
4.1%
1923
 
3.9%
2310
 
1.7%
248
 
1.4%
215
 
0.8%
164
 
0.7%
173
 
0.5%
253
 
0.5%
Other values (3)4
 
0.7%
(Missing)266
45.0%
ValueCountFrequency (%)
151
 
0.2%
164
 
0.7%
173
 
0.5%
18104
17.6%
1923
 
3.9%
ValueCountFrequency (%)
281
 
0.2%
262
 
0.3%
253
 
0.5%
248
1.4%
2310
1.7%

restlessness
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing264
Missing (%)44.7%
Memory size4.7 KiB
False
324 
True
 
3
(Missing)
264 
ValueCountFrequency (%)
False324
54.8%
True3
 
0.5%
(Missing)264
44.7%
2021-02-12T10:50:21.601408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

rhinitis
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing265
Missing (%)44.8%
Memory size4.7 KiB
False
321 
True
 
5
(Missing)
265 
ValueCountFrequency (%)
False321
54.3%
True5
 
0.8%
(Missing)265
44.8%
2021-02-12T10:50:21.639024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sbp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct63
Distinct (%)19.3%
Missing265
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean98.76380368
Minimum50
Maximum150
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:21.719650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q190
median100
Q3110
95-th percentile130
Maximum150
Range100
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.43246383
Coefficient of variation (CV)0.2068821073
Kurtosis-0.2560608251
Mean98.76380368
Median Absolute Deviation (MAD)10
Skewness-0.494139485
Sum32197
Variance417.4855781
MonotocityNot monotonic
2021-02-12T10:50:21.843557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10062
 
10.5%
11054
 
9.1%
12023
 
3.9%
9017
 
2.9%
13013
 
2.2%
6012
 
2.0%
708
 
1.4%
1058
 
1.4%
735
 
0.8%
955
 
0.8%
Other values (53)119
20.1%
(Missing)265
44.8%
ValueCountFrequency (%)
502
0.3%
551
 
0.2%
572
0.3%
583
0.5%
594
0.7%
ValueCountFrequency (%)
1502
0.3%
1451
0.2%
1361
0.2%
1351
0.2%
1332
0.3%

serology_interpretation
Categorical

MISSING

Distinct3
Distinct (%)4.2%
Missing519
Missing (%)87.8%
Memory size4.7 KiB
Probable Secondary
35 
Inconclusive
31 
Probable primary

Length

Max length18
Median length16
Mean length15.25
Min length12

Characters and Unicode

Total characters1098
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInconclusive
2nd rowInconclusive
3rd rowInconclusive
4th rowInconclusive
5th rowInconclusive
ValueCountFrequency (%)
Probable Secondary35
 
5.9%
Inconclusive31
 
5.2%
Probable primary6
 
1.0%
(Missing)519
87.8%
2021-02-12T10:50:22.074367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:22.140229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
probable41
36.3%
secondary35
31.0%
inconclusive31
27.4%
primary6
 
5.3%

Most occurring characters

ValueCountFrequency (%)
o107
 
9.7%
e107
 
9.7%
n97
 
8.8%
c97
 
8.8%
r88
 
8.0%
b82
 
7.5%
a82
 
7.5%
l72
 
6.6%
P41
 
3.7%
41
 
3.7%
Other values (10)284
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter950
86.5%
Uppercase Letter107
 
9.7%
Space Separator41
 
3.7%

Most frequent character per category

ValueCountFrequency (%)
o107
11.3%
e107
11.3%
n97
10.2%
c97
10.2%
r88
9.3%
b82
8.6%
a82
8.6%
l72
7.6%
y41
 
4.3%
i37
 
3.9%
Other values (6)140
14.7%
ValueCountFrequency (%)
P41
38.3%
S35
32.7%
I31
29.0%
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1057
96.3%
Common41
 
3.7%

Most frequent character per script

ValueCountFrequency (%)
o107
10.1%
e107
10.1%
n97
 
9.2%
c97
 
9.2%
r88
 
8.3%
b82
 
7.8%
a82
 
7.8%
l72
 
6.8%
P41
 
3.9%
y41
 
3.9%
Other values (9)243
23.0%
ValueCountFrequency (%)
41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1098
100.0%

Most frequent character per block

ValueCountFrequency (%)
o107
 
9.7%
e107
 
9.7%
n97
 
8.8%
c97
 
8.8%
r88
 
8.0%
b82
 
7.5%
a82
 
7.5%
l72
 
6.6%
P41
 
3.7%
41
 
3.7%
Other values (10)284
25.9%

shock
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size719.0 B
False
568 
True
 
23
ValueCountFrequency (%)
False568
96.1%
True23
 
3.9%
2021-02-12T10:50:22.182440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

shock_resucitation
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
143 
True
 
4
(Missing)
444 
ValueCountFrequency (%)
False143
 
24.2%
True4
 
0.7%
(Missing)444
75.1%
2021-02-12T10:50:22.218167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_describe
Categorical

MISSING

Distinct5
Distinct (%)41.7%
Missing579
Missing (%)98.0%
Memory size4.7 KiB
MACULAR
RECOVERY
MACULOPAPULAR
MACULER
MACULAR

Length

Max length13
Median length7.5
Mean length8.333333333
Min length7

Characters and Unicode

Total characters100
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st rowMACULOPAPULAR
2nd rowMACULOPAPULAR
3rd rowMACULAR
4th rowRECOVERY
5th rowMACULAR
ValueCountFrequency (%)
MACULAR5
 
0.8%
RECOVERY3
 
0.5%
MACULOPAPULAR2
 
0.3%
MACULER1
 
0.2%
MACULAR 1
 
0.2%
(Missing)579
98.0%
2021-02-12T10:50:22.404258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:22.470039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
macular6
50.0%
recovery3
25.0%
maculopapular2
 
16.7%
maculer1
 
8.3%

Most occurring characters

ValueCountFrequency (%)
A19
19.0%
R15
15.0%
C12
12.0%
U11
11.0%
L11
11.0%
M9
9.0%
E7
 
7.0%
O5
 
5.0%
P4
 
4.0%
V3
 
3.0%
Other values (2)4
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter99
99.0%
Space Separator1
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
A19
19.2%
R15
15.2%
C12
12.1%
U11
11.1%
L11
11.1%
M9
9.1%
E7
 
7.1%
O5
 
5.1%
P4
 
4.0%
V3
 
3.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin99
99.0%
Common1
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
A19
19.2%
R15
15.2%
C12
12.1%
U11
11.1%
L11
11.1%
M9
9.1%
E7
 
7.1%
O5
 
5.1%
P4
 
4.0%
V3
 
3.0%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ValueCountFrequency (%)
A19
19.0%
R15
15.0%
C12
12.0%
U11
11.0%
L11
11.0%
M9
9.0%
E7
 
7.0%
O5
 
5.0%
P4
 
4.0%
V3
 
3.0%
Other values (2)4
 
4.0%

skin_flush
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
307 
True
281 
(Missing)
 
3
ValueCountFrequency (%)
False307
51.9%
True281
47.5%
(Missing)3
 
0.5%
2021-02-12T10:50:22.520027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_rash
Boolean

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.5%
Memory size4.7 KiB
False
435 
True
153 
(Missing)
 
3
ValueCountFrequency (%)
False435
73.6%
True153
 
25.9%
(Missing)3
 
0.5%
2021-02-12T10:50:22.554816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

treatment_haemorrhage
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing444
Missing (%)75.1%
Memory size4.7 KiB
False
146 
True
 
1
(Missing)
444 
ValueCountFrequency (%)
False146
 
24.7%
True1
 
0.2%
(Missing)444
75.1%
2021-02-12T10:50:22.594078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting
Boolean

MISSING

Distinct2
Distinct (%)0.4%
Missing82
Missing (%)13.9%
Memory size4.7 KiB
False
331 
True
178 
(Missing)
82 
ValueCountFrequency (%)
False331
56.0%
True178
30.1%
(Missing)82
 
13.9%
2021-02-12T10:50:22.627431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting_level
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
nan
560 
1.0
 
18
2.0
 
12
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan560
94.8%
1.018
 
3.0%
2.012
 
2.0%
3.01
 
0.2%
2021-02-12T10:50:22.784987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:50:22.839814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan560
94.8%
1.018
 
3.0%
2.012
 
2.0%
3.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n1120
63.2%
a560
31.6%
.31
 
1.7%
031
 
1.7%
118
 
1.0%
212
 
0.7%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1680
94.8%
Decimal Number62
 
3.5%
Other Punctuation31
 
1.7%

Most frequent character per category

ValueCountFrequency (%)
031
50.0%
118
29.0%
212
 
19.4%
31
 
1.6%
ValueCountFrequency (%)
n1120
66.7%
a560
33.3%
ValueCountFrequency (%)
.31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1680
94.8%
Common93
 
5.2%

Most frequent character per script

ValueCountFrequency (%)
.31
33.3%
031
33.3%
118
19.4%
212
 
12.9%
31
 
1.1%
ValueCountFrequency (%)
n1120
66.7%
a560
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1120
63.2%
a560
31.6%
.31
 
1.7%
031
 
1.7%
118
 
1.0%
212
 
0.7%
31
 
0.1%

wbc
Real number (ℝ≥0)

MISSING

Distinct92
Distinct (%)27.5%
Missing256
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean4.848656716
Minimum0.9
Maximum41.6
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:22.936933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.8
Q13
median4.3
Q35.7
95-th percentile9.6
Maximum41.6
Range40.7
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation3.140901171
Coefficient of variation (CV)0.6477879039
Kurtosis55.96581435
Mean4.848656716
Median Absolute Deviation (MAD)1.3
Skewness5.377124519
Sum1624.3
Variance9.865260166
MonotocityNot monotonic
2021-02-12T10:50:23.076810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.211
 
1.9%
3.810
 
1.7%
2.79
 
1.5%
3.69
 
1.5%
3.78
 
1.4%
3.98
 
1.4%
4.78
 
1.4%
2.68
 
1.4%
4.68
 
1.4%
4.98
 
1.4%
Other values (82)248
42.0%
(Missing)256
43.3%
ValueCountFrequency (%)
0.91
0.2%
11
0.2%
1.12
0.3%
1.32
0.3%
1.41
0.2%
ValueCountFrequency (%)
41.61
0.2%
15.21
0.2%
141
0.2%
13.91
0.2%
13.71
0.2%

weight
Real number (ℝ≥0)

Distinct32
Distinct (%)5.4%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean54.55612245
Minimum18
Maximum110
Zeros0
Zeros (%)0.0%
Memory size4.7 KiB
2021-02-12T10:50:23.191736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile35
Q145
median53
Q364
95-th percentile76
Maximum110
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.18142053
Coefficient of variation (CV)0.2599418707
Kurtosis2.758404468
Mean54.55612245
Median Absolute Deviation (MAD)8
Skewness0.8828774741
Sum32079
Variance201.1126882
MonotocityNot monotonic
2021-02-12T10:50:23.303528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5065
 
11.0%
4244
 
7.4%
5541
 
6.9%
5738
 
6.4%
5234
 
5.8%
7030
 
5.1%
4528
 
4.7%
6527
 
4.6%
5626
 
4.4%
6621
 
3.6%
Other values (22)234
39.6%
ValueCountFrequency (%)
187
 
1.2%
259
1.5%
3515
2.5%
3720
3.4%
3912
2.0%
ValueCountFrequency (%)
1108
1.4%
968
1.4%
809
1.5%
768
1.4%
748
1.4%

day_from_enrolment
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)7.5%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean-11.35204082
Minimum-4384
Maximum2916
Zeros72
Zeros (%)12.2%
Memory size4.7 KiB
2021-02-12T10:50:23.413566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4384
5-th percentile-5
Q1-1
median1
Q33
95-th percentile13.65
Maximum2916
Range7300
Interquartile range (IQR)4

Descriptive statistics

Standard deviation345.5254846
Coefficient of variation (CV)-30.43730112
Kurtosis139.4564457
Mean-11.35204082
Median Absolute Deviation (MAD)2
Skewness-9.062738667
Sum-6675
Variance119387.8605
MonotocityNot monotonic
2021-02-12T10:50:23.532286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
072
12.2%
170
11.8%
267
11.3%
-164
10.8%
358
9.8%
446
7.8%
-236
 
6.1%
-426
 
4.4%
-325
 
4.2%
-522
 
3.7%
Other values (34)102
17.3%
ValueCountFrequency (%)
-43841
0.2%
-43821
0.2%
-43801
0.2%
-921
0.2%
-881
0.2%
ValueCountFrequency (%)
29161
0.2%
18271
0.2%
7331
0.2%
3691
0.2%
631
0.2%

day_from_admission
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)7.1%
Missing3
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean-10.31122449
Minimum-4381
Maximum2918
Zeros72
Zeros (%)12.2%
Memory size4.7 KiB
2021-02-12T10:50:23.651260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4381
5-th percentile-4
Q10
median2
Q34
95-th percentile14.65
Maximum2918
Range7299
Interquartile range (IQR)4

Descriptive statistics

Standard deviation345.497666
Coefficient of variation (CV)-33.50694831
Kurtosis139.4380418
Mean-10.31122449
Median Absolute Deviation (MAD)2
Skewness-9.059465789
Sum-6063
Variance119368.6372
MonotocityNot monotonic
2021-02-12T10:50:23.768296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
072
12.2%
269
11.7%
368
11.5%
167
11.3%
458
9.8%
544
7.4%
-135
 
5.9%
-325
 
4.2%
-224
 
4.1%
-424
 
4.1%
Other values (32)102
17.3%
ValueCountFrequency (%)
-43812
0.3%
-43791
0.2%
-911
0.2%
-881
0.2%
-231
0.2%
ValueCountFrequency (%)
29181
0.2%
18281
0.2%
7341
0.2%
3701
0.2%
641
0.2%

Interactions

2021-02-12T10:49:12.740098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:12.807051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:12.870677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:12.940839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.010864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.074571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.136745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.203135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.279539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.351772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.431927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.496764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.569951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.641171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.715237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.791666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.858821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:13.930762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.004400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.075630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.149372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.218330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.295672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.373015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.454718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.538150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.599074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.657709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.728589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.793723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.854935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.916965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:14.972866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.038048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.107515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.170562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.236969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.302494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.367931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.433145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.500096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.568378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.631147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.695026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.764881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.828280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.895656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:15.960684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.019601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.083521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.150231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.209337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.274252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.339163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.405649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.472637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.528175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.588059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.652260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.717762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.781350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.838665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.904520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:16.970210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.028126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.093139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.158445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.223683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.292486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.353079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.415010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.474056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.534632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.590270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.655502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.717820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.787653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.858101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:17.924585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:18.004238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:18.066402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:18.131423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:18.196226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:49:18.267744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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